Project 4: Using a prospective cohort survey to test population-level predictions generated by Projects 1-3

项目摘要。烟草制品研究中心（CSTP）开发了一个模型， 使用电子香烟（ECIG）作为范例评估新型烟草产品，所述电子香烟加热液体， 通常含有尼古丁，形成使用者吸入的气雾剂。现在，CSTP利用其方法和 ECIG专业知识从产品评估转向影响分析的综合主题。具体而言是 CSTP提出了FDA可以预测潜在法规影响的方法， 然后可以检验预测的效应是否发生在群体中。CSTP模型 评估潜在法规如何影响产品毒性（项目1）、用户行为（项目2），以及 产品成瘾/滥用责任（项目3），这些项目将共同有助于预测 人口现象。项目4将检验这些人口预测的有效性。 FDA法规旨在促进健康，但也可能产生意想不到的后果。比如说， 如在欧盟（EU）中那样，将ECIG液体限制为<20 mg/ml尼古丁，可以推动高功率ECIG的使用 使更多的液体雾化，导致使用者吸入更多的尼古丁。也可能出现意想不到的后果 来自其他作用，如限制ECIG尼古丁通量（单位时间内释放的尼古丁）或减少风味 空房的检查这些和其他潜在的法规如何影响产品毒性，用户行为， 受控环境中的产品滥用责任可导致对人口一级后果的预测。 然而，这些预测的准确性需要检验。烟草制品对人口的影响 可在规章实施后，利用大规模纵向调查对规章进行测量。项目 4个具体目标是测试项目1-3的产品对照研究中产生的人群水平预测。 毒性、使用者行为和产品滥用倾向，使用前瞻性队列调查，包括当前 单独ECIG使用者（N=600）和目前ECIG和烟草香烟的双重使用者（N=600）。具体地说， 该研究将评估三种潜在监管行动的人群水平影响：（1）尼古丁限制，（2） 限制尼古丁通量，和（3）减少香料的可用性。项目4涉及以下科学领域： 影响分析及其工具由背景知识核心提供信息，这将有助于确定 如何准确评估自我报告，并可靠地评估ECIG用户的依赖性。总体而言，结果将 揭示在受控条件下进行的观察在多大程度上产生有效的群体水平 预测。因此，该项目是一个中心的一部分，其综合主题是影响分析， 该团队在评估烟草管制对人群水平影响方面的专业知识，并将为FDA提供一套工具 这可以用来指导法规的制定，以便在法规生效时， 方法已被用来测试它，完善它，并产生数据，表明其促进健康的效果， 最大化和最小化意外后果。

Project Summary. The Center for the Study of Tobacco Products (CSTP) has developed a model for evaluating novel tobacco products using, as exemplars, electronic cigarettes (ECIGs) that heat a liquid that often contains nicotine, forming an aerosol that users inhale. Now, CSTP leverages its methodological and ECIG expertise to pivot from product evaluation to an integrative theme of impact analysis. Specifically, the CSTP proposes methods with which FDA can generate predictions regarding a potential regulation’s effects, and then whether or not the predicted effects occur in the population can be tested. The CSTP’s model assesses how potential regulation might influence product toxicity (Project 1), user behavior (Project 2), and product addiction/abuse liability (Project 3) and, together, these projects will contribute to predictions regarding population-level phenomena. Project 4 will test the validity of those population-level predictions. FDA regulations are intended to promote health, but also may have unintended consequences. For example, limiting ECIG liquids to <20 mg/ml nicotine, as in the European Union (EU), can drive use of high power ECIGs that aerosolize more liquid, leading users to inhale more nicotine. Unintended consequences may also occur from other action, such as constraining ECIG nicotine flux (emitted nicotine/unit time), or reducing flavor availability. Examining how these and other potential regulations influence product toxicity, user behavior, and product abuse liability in controlled settings can lead to predictions regarding population-level consequences. However, the accuracy of these predictions should be tested. Population-level effects of tobacco product regulation can be measured after the regulation is put into place using large-scale longitudinal surveys. Project 4 specific aims are to test population-level predictions that arise from Project 1-3’s controlled studies of product toxicity, user behavior, and product abuse liability using a prospective cohort survey that includes current exclusive ECIG users (N=600) and current dual users of ECIGs and tobacco cigarettes (N=600). Specifically, the study will assess the population-level effects of three potential regulatory actions: (1) limits on nicotine, (2) constraints on nicotine flux, and (3) reduction in flavor availability. Project 4 addresses the scientific domain of impact analysis and its instruments are informed by the Contextual Knowledge Core that will help determine how to assess self-report accurately and assess dependence reliably among ECIG users. Overall, results will reveal the extent to which observations made under controlled conditions yield valid population-level predictions. Thus, this project is part of a center with an integrative theme of impact analysis that draws on the team’s expertise in evaluating population-level effects of tobacco regulation and will provide FDA a set of tools that can be used to guide regulation development so that, by the time a regulation goes into effect, validated methods have been used to test it, refine it, and generate data that show that its health-promoting effects are maximized and unintended consequences are minimized.